Buddha once said, "To reach Enlightenment, you must turn data into insight and insight into action". Ok, he didn't say that, but Knowi can help you blend hindsight with foresight and drive actions from your data.
Currently, Knowi supports Classification, Regression and Time-Series Anomaly Detection type Machine Learning use cases, with clustering and deep learning coming soon. We also have a data preparation wizard that will guide you through the steps necessary to clean your data prior to any supervised modelling activities.
Anomaly detection is often used to identify unusual patterns that do not conform to expected behavior (called outliers).
There ares many applications in business, from intrusion detection to system health monitoring and from fraud detection in credit card transactions to fault detection in operating environments.
For supervised learning, algorithms are selected based on the type of prediction response:
- if your response is continuous numbers, then you will be using regression algorithms.
- if your response is categories or classes, then you will be using classification algorithms.
For example, if you are building a model to predict the $ amount by which a person is likely to default on a credit card payment, then it's regression. However, if your you just want to know if they are likely to default or not then it's classification.
To start the Machine Learning process, simply select the Machine Learning icon, create your workspace and let Knowi guide you through the steps required to create your Machine Learning models!
Trigger Notification and Actions
Triggers and actions can be applied to the results. For example, you can send an alert or a webhook into your application for the users with a high risk of default for the use case above. The process for setting up triggers and alerts on a query with machine learning remains the same as a normal dataset/query. For more details, see Alerts.